Identification of Treatment Effects under Conditional Partial\n Independence

2017/07/29 by Matthew A. Masten, Masten, Matthew A., Alexandre Poirier +1 · 2 citations
Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.1707.09563

openalex publication_date 2017/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Conditional independence of treatment assignment from potential outcomes is a\ncommonly used but nonrefutable assumption. We derive identified sets for\nvarious treatment effect parameters under nonparametric deviations from this\nconditional independence assumption. These deviations are defined via a\nconditional treatment assignment probability, which makes it straightforward to\ninterpret. Our results can be used to assess the robustness of empirical\nconclusions obtained under the baseline conditional independence assumption.\n

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